Interpretable multi-scale graph descriptors via structural compression

Autor: Zohair Raza Hassan, Mudassir Shabbir, Ammar Ahmed
Rok vydání: 2020
Předmět:
Zdroj: Information Sciences. 533:169-180
ISSN: 0020-0255
DOI: 10.1016/j.ins.2020.05.032
Popis: Graph representations that preserve relevant topological information allow the use of a rich machine learning toolset for data-driven network analytics. Some notable graph representations in the literature are fruitful in their respective applications but they either lack interpretability or are unable to effectively encode a graph’s structure at both local and global scale. In this work, we propose the Higher-Order Structure Descriptor (HOSD): an interpretable graph descriptor that captures information about the patterns in a graph at multiple scales. Scaling is achieved using a novel graph compression technique that reveals successive higher-order structures. The proposed descriptor is invariant to node permutations due to its graph-theoretic nature. We analyze the HOSD algorithm for time complexity and also prove the NP-completeness of three interesting graph compression problems. A faster version, HOSD-Lite, is also presented to approximate HOSD on dense graphs. We showcase the interpretability of our model by discussing structural patterns found within real-world datasets using HOSD. HOSD and HOSD-Lite are evaluated on benchmark datasets for applicability to classification problems; results demonstrate that a simple random forest setup based on our representations competes well with the current state-of-the-art graph embeddings.
Databáze: OpenAIRE
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